Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations528005
Missing cells475317
Missing cells (%)3.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory99.7 MiB
Average record size in memory198.0 B

Variable types

Numeric6
Categorical11
Text7
DateTime2

Alerts

auth is highly overall correlated with length and 1 other fieldsHigh correlation
browser is highly overall correlated with device and 1 other fieldsHigh correlation
country is highly overall correlated with region and 1 other fieldsHigh correlation
device is highly overall correlated with browser and 1 other fieldsHigh correlation
gender is highly overall correlated with userIdHigh correlation
length is highly overall correlated with auth and 3 other fieldsHigh correlation
level is highly overall correlated with userIdHigh correlation
method is highly overall correlated with length and 1 other fieldsHigh correlation
os is highly overall correlated with region and 1 other fieldsHigh correlation
page is highly overall correlated with auth and 3 other fieldsHigh correlation
region is highly overall correlated with country and 2 other fieldsHigh correlation
sessionId is highly overall correlated with ts and 1 other fieldsHigh correlation
status is highly overall correlated with length and 1 other fieldsHigh correlation
ts is highly overall correlated with sessionIdHigh correlation
userId is highly overall correlated with browser and 7 other fieldsHigh correlation
page is highly imbalanced (70.5%)Imbalance
auth is highly imbalanced (99.7%)Imbalance
method is highly imbalanced (62.1%)Imbalance
status is highly imbalanced (73.2%)Imbalance
device is highly imbalanced (68.2%)Imbalance
country is highly imbalanced (58.9%)Imbalance
artist has 95128 (18.0%) missing valuesMissing
song has 95128 (18.0%) missing valuesMissing
length has 95128 (18.0%) missing valuesMissing
browser has 37918 (7.2%) missing valuesMissing
region has 152015 (28.8%) missing valuesMissing

Reproduction

Analysis started2025-10-06 11:01:42.548298
Analysis finished2025-10-06 11:02:03.724256
Duration21.18 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

ts
Real number (ℝ)

High correlation 

Distinct499351
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5409669 × 1012
Minimum1.538352 × 1012
Maximum1.5436225 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2025-10-06T14:02:03.757409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.538352 × 1012
5-th percentile1.5386161 × 1012
Q11.539723 × 1012
median1.541007 × 1012
Q31.5421782 × 1012
95-th percentile1.5433659 × 1012
Maximum1.5436225 × 1012
Range5.270455 × 109
Interquartile range (IQR)2.455156 × 109

Descriptive statistics

Standard deviation1.4812331 × 109
Coefficient of variation (CV)0.00096123614
Kurtosis-1.1111058
Mean1.5409669 × 1012
Median Absolute Deviation (MAD)1.224116 × 109
Skewness0.0034125876
Sum8.1363824 × 1017
Variance2.1940515 × 1018
MonotonicityIncreasing
2025-10-06T14:02:03.791562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15409918450004
 
< 0.1%
15434977410004
 
< 0.1%
15385272620004
 
< 0.1%
15415482740004
 
< 0.1%
15387444890004
 
< 0.1%
15408334470004
 
< 0.1%
15422998420004
 
< 0.1%
15410071800004
 
< 0.1%
15420529280004
 
< 0.1%
15405780510004
 
< 0.1%
Other values (499341)527965
> 99.9%
ValueCountFrequency (%)
15383520110001
< 0.1%
15383520250001
< 0.1%
15383521180001
< 0.1%
15383521190001
< 0.1%
15383521240001
< 0.1%
15383521250001
< 0.1%
15383521760001
< 0.1%
15383522150001
< 0.1%
15383522410001
< 0.1%
15383522590001
< 0.1%
ValueCountFrequency (%)
15436224660001
< 0.1%
15436224050001
< 0.1%
15436224040001
< 0.1%
15436224010001
< 0.1%
15436223930001
< 0.1%
15436223920001
< 0.1%
15436223790002
< 0.1%
15436223780001
< 0.1%
15436223760001
< 0.1%
15436223680001
< 0.1%

userId
Real number (ℝ)

High correlation 

Distinct448
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60268.427
Minimum2
Maximum300051
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2025-10-06T14:02:03.822413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile24
Q196
median201
Q3100008
95-th percentile300035
Maximum300051
Range300049
Interquartile range (IQR)99912

Descriptive statistics

Standard deviation109898.82
Coefficient of variation (CV)1.8234892
Kurtosis0.39643461
Mean60268.427
Median Absolute Deviation (MAD)106
Skewness1.4638795
Sum3.1822031 × 1010
Variance1.2077751 × 1010
MonotonicityNot monotonic
2025-10-06T14:02:03.853141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
929767
 
1.8%
1407448
 
1.4%
3000497309
 
1.4%
1016842
 
1.3%
3000356810
 
1.3%
1956184
 
1.2%
2306019
 
1.1%
1635965
 
1.1%
2505678
 
1.1%
185511
 
1.0%
Other values (438)460472
87.2%
ValueCountFrequency (%)
2941
 
0.2%
325
 
< 0.1%
4542
 
0.1%
5278
 
0.1%
61408
0.3%
7600
 
0.1%
832
 
< 0.1%
92596
0.5%
10423
 
0.1%
11264
 
< 0.1%
ValueCountFrequency (%)
3000511108
 
0.2%
30005079
 
< 0.1%
3000497309
1.4%
3000481456
 
0.3%
300047655
 
0.1%
300046790
 
0.1%
300045889
 
0.2%
3000441081
 
0.2%
30004353
 
< 0.1%
3000421705
 
0.3%

sessionId
Real number (ℝ)

High correlation 

Distinct4470
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2042.9802
Minimum1
Maximum4808
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2025-10-06T14:02:03.883358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile153
Q1632
median1973
Q33310
95-th percentile4364
Maximum4808
Range4807
Interquartile range (IQR)2678

Descriptive statistics

Standard deviation1433.9981
Coefficient of variation (CV)0.70191486
Kurtosis-1.3291119
Mean2042.9802
Median Absolute Deviation (MAD)1339
Skewness0.20244849
Sum1.0787038 × 109
Variance2056350.7
MonotonicityNot monotonic
2025-10-06T14:02:03.914002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1052987
 
0.2%
2860912
 
0.2%
3425884
 
0.2%
4131873
 
0.2%
477817
 
0.2%
2470799
 
0.2%
3513765
 
0.1%
836746
 
0.1%
3821739
 
0.1%
356731
 
0.1%
Other values (4460)519752
98.4%
ValueCountFrequency (%)
1173
 
< 0.1%
254
 
< 0.1%
386
 
< 0.1%
496
 
< 0.1%
5119
 
< 0.1%
6119
 
< 0.1%
722
 
< 0.1%
9627
0.1%
10285
0.1%
11277
0.1%
ValueCountFrequency (%)
480814
 
< 0.1%
480124
 
< 0.1%
479954
 
< 0.1%
479197
 
< 0.1%
479084
 
< 0.1%
477938
 
< 0.1%
4777132
< 0.1%
477439
 
< 0.1%
4771148
< 0.1%
4767244
< 0.1%

page
Categorical

High correlation  Imbalance 

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
NextSong
432877 
Thumbs Up
 
23826
Home
 
19089
Add to Playlist
 
12349
Add Friend
 
8087
Other values (14)
 
31777

Length

Max length25
Median length8
Mean length8.1342923
Min length4

Characters and Unicode

Total characters4294947
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNextSong
2nd rowNextSong
3rd rowNextSong
4th rowLogout
5th rowNextSong

Common Values

ValueCountFrequency (%)
NextSong432877
82.0%
Thumbs Up23826
 
4.5%
Home19089
 
3.6%
Add to Playlist12349
 
2.3%
Add Friend8087
 
1.5%
Roll Advert7773
 
1.5%
Logout5990
 
1.1%
Thumbs Down4911
 
0.9%
Downgrade3811
 
0.7%
Settings2964
 
0.6%
Other values (9)6328
 
1.2%

Length

2025-10-06T14:02:03.942129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nextsong432877
72.3%
thumbs28737
 
4.8%
up23826
 
4.0%
add20436
 
3.4%
home19089
 
3.2%
to12349
 
2.1%
playlist12349
 
2.1%
friend8087
 
1.4%
roll7773
 
1.3%
advert7773
 
1.3%
Other values (13)25092
 
4.2%

Most occurring characters

ValueCountFrequency (%)
o494733
11.5%
t480064
11.2%
e479985
11.2%
n453847
10.6%
g447599
10.4%
S437415
10.2%
N432877
10.1%
x432877
10.1%
70383
 
1.6%
d61915
 
1.4%
Other values (26)503252
11.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)4294947
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o494733
11.5%
t480064
11.2%
e479985
11.2%
n453847
10.6%
g447599
10.4%
S437415
10.2%
N432877
10.1%
x432877
10.1%
70383
 
1.6%
d61915
 
1.4%
Other values (26)503252
11.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4294947
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o494733
11.5%
t480064
11.2%
e479985
11.2%
n453847
10.6%
g447599
10.4%
S437415
10.2%
N432877
10.1%
x432877
10.1%
70383
 
1.6%
d61915
 
1.4%
Other values (26)503252
11.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4294947
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o494733
11.5%
t480064
11.2%
e479985
11.2%
n453847
10.6%
g447599
10.4%
S437415
10.2%
N432877
10.1%
x432877
10.1%
70383
 
1.6%
d61915
 
1.4%
Other values (26)503252
11.7%

auth
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
Logged In
527906 
Cancelled
 
99

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters4752045
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLogged In
2nd rowLogged In
3rd rowLogged In
4th rowLogged In
5th rowLogged In

Common Values

ValueCountFrequency (%)
Logged In527906
> 99.9%
Cancelled99
 
< 0.1%

Length

2025-10-06T14:02:04.019350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T14:02:04.037696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
logged527906
50.0%
in527906
50.0%
cancelled99
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
g1055812
22.2%
e528104
11.1%
d528005
11.1%
n528005
11.1%
L527906
11.1%
o527906
11.1%
527906
11.1%
I527906
11.1%
l198
 
< 0.1%
C99
 
< 0.1%
Other values (2)198
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)4752045
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
g1055812
22.2%
e528104
11.1%
d528005
11.1%
n528005
11.1%
L527906
11.1%
o527906
11.1%
527906
11.1%
I527906
11.1%
l198
 
< 0.1%
C99
 
< 0.1%
Other values (2)198
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4752045
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
g1055812
22.2%
e528104
11.1%
d528005
11.1%
n528005
11.1%
L527906
11.1%
o527906
11.1%
527906
11.1%
I527906
11.1%
l198
 
< 0.1%
C99
 
< 0.1%
Other values (2)198
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4752045
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
g1055812
22.2%
e528104
11.1%
d528005
11.1%
n528005
11.1%
L527906
11.1%
o527906
11.1%
527906
11.1%
I527906
11.1%
l198
 
< 0.1%
C99
 
< 0.1%
Other values (2)198
 
< 0.1%

method
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
PUT
489128 
GET
 
38877

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1584015
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPUT
2nd rowPUT
3rd rowPUT
4th rowPUT
5th rowPUT

Common Values

ValueCountFrequency (%)
PUT489128
92.6%
GET38877
 
7.4%

Length

2025-10-06T14:02:04.055737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T14:02:04.070568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
put489128
92.6%
get38877
 
7.4%

Most occurring characters

ValueCountFrequency (%)
T528005
33.3%
P489128
30.9%
U489128
30.9%
G38877
 
2.5%
E38877
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)1584015
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T528005
33.3%
P489128
30.9%
U489128
30.9%
G38877
 
2.5%
E38877
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1584015
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T528005
33.3%
P489128
30.9%
U489128
30.9%
G38877
 
2.5%
E38877
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1584015
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T528005
33.3%
P489128
30.9%
U489128
30.9%
G38877
 
2.5%
E38877
 
2.5%

status
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
200
483600 
307
 
43902
404
 
503

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1584015
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row200
2nd row200
3rd row200
4th row307
5th row200

Common Values

ValueCountFrequency (%)
200483600
91.6%
30743902
 
8.3%
404503
 
0.1%

Length

2025-10-06T14:02:04.087830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T14:02:04.103067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
200483600
91.6%
30743902
 
8.3%
404503
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01011605
63.9%
2483600
30.5%
343902
 
2.8%
743902
 
2.8%
41006
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1584015
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01011605
63.9%
2483600
30.5%
343902
 
2.8%
743902
 
2.8%
41006
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1584015
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01011605
63.9%
2483600
30.5%
343902
 
2.8%
743902
 
2.8%
41006
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1584015
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01011605
63.9%
2483600
30.5%
343902
 
2.8%
743902
 
2.8%
41006
 
0.1%

level
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size515.9 KiB
paid
418044 
free
109961 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters2112020
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfree
2nd rowfree
3rd rowpaid
4th rowpaid
5th rowpaid

Common Values

ValueCountFrequency (%)
paid418044
79.2%
free109961
 
20.8%

Length

2025-10-06T14:02:04.122969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T14:02:04.137177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
paid418044
79.2%
free109961
 
20.8%

Most occurring characters

ValueCountFrequency (%)
p418044
19.8%
a418044
19.8%
i418044
19.8%
d418044
19.8%
e219922
10.4%
f109961
 
5.2%
r109961
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)2112020
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p418044
19.8%
a418044
19.8%
i418044
19.8%
d418044
19.8%
e219922
10.4%
f109961
 
5.2%
r109961
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2112020
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p418044
19.8%
a418044
19.8%
i418044
19.8%
d418044
19.8%
e219922
10.4%
f109961
 
5.2%
r109961
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2112020
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p418044
19.8%
a418044
19.8%
i418044
19.8%
d418044
19.8%
e219922
10.4%
f109961
 
5.2%
r109961
 
5.2%

itemInSession
Real number (ℝ)

Distinct1006
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.77899
Minimum0
Maximum1005
Zeros5147
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2025-10-06T14:02:04.159817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q127
median68
Q3148
95-th percentile349
Maximum1005
Range1005
Interquartile range (IQR)121

Descriptive statistics

Standard deviation116.86479
Coefficient of variation (CV)1.0843002
Kurtosis5.6652906
Mean107.77899
Median Absolute Deviation (MAD)50
Skewness2.0657049
Sum56907848
Variance13657.378
MonotonicityNot monotonic
2025-10-06T14:02:04.189966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25691
 
1.1%
35646
 
1.1%
45586
 
1.1%
55522
 
1.0%
65431
 
1.0%
15401
 
1.0%
75330
 
1.0%
85258
 
1.0%
05147
 
1.0%
95146
 
1.0%
Other values (996)473847
89.7%
ValueCountFrequency (%)
05147
1.0%
15401
1.0%
25691
1.1%
35646
1.1%
45586
1.1%
55522
1.0%
65431
1.0%
75330
1.0%
85258
1.0%
95146
1.0%
ValueCountFrequency (%)
10051
< 0.1%
10041
< 0.1%
10031
< 0.1%
10021
< 0.1%
10011
< 0.1%
10001
< 0.1%
9991
< 0.1%
9981
< 0.1%
9971
< 0.1%
9961
< 0.1%
Distinct192
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
2025-10-06T14:02:04.276431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length46
Median length39
Mean length25.48594
Min length8

Characters and Unicode

Total characters13456704
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCorpus Christi, TX
2nd rowHouston-The Woodlands-Sugar Land, TX
3rd rowOrlando-Kissimmee-Sanford, FL
4th rowOrlando-Kissimmee-Sanford, FL
5th rowMobile, AL
ValueCountFrequency (%)
ca78171
 
5.5%
city51680
 
3.6%
new45353
 
3.2%
tx42442
 
3.0%
ny-nj-pa40156
 
2.8%
york-newark-jersey40156
 
2.8%
los34278
 
2.4%
angeles-long34278
 
2.4%
beach-anaheim34278
 
2.4%
fl32129
 
2.3%
Other values (301)985094
69.5%
2025-10-06T14:02:04.398102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e969488
 
7.2%
890010
 
6.6%
a877940
 
6.5%
-866585
 
6.4%
o742296
 
5.5%
n722917
 
5.4%
r620281
 
4.6%
i553202
 
4.1%
l546456
 
4.1%
,528005
 
3.9%
Other values (47)6139524
45.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)13456704
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e969488
 
7.2%
890010
 
6.6%
a877940
 
6.5%
-866585
 
6.4%
o742296
 
5.5%
n722917
 
5.4%
r620281
 
4.6%
i553202
 
4.1%
l546456
 
4.1%
,528005
 
3.9%
Other values (47)6139524
45.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13456704
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e969488
 
7.2%
890010
 
6.6%
a877940
 
6.5%
-866585
 
6.4%
o742296
 
5.5%
n722917
 
5.4%
r620281
 
4.6%
i553202
 
4.1%
l546456
 
4.1%
,528005
 
3.9%
Other values (47)6139524
45.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13456704
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e969488
 
7.2%
890010
 
6.6%
a877940
 
6.5%
-866585
 
6.4%
o742296
 
5.5%
n722917
 
5.4%
r620281
 
4.6%
i553202
 
4.1%
l546456
 
4.1%
,528005
 
3.9%
Other values (47)6139524
45.6%
Distinct71
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
2025-10-06T14:02:04.472817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length139
Median length130
Mean length104.43651
Min length63

Characters and Unicode

Total characters55142997
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.125 Safari/537.36"
2nd row"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.143 Safari/537.36"
3rd row"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.143 Safari/537.36"
4th row"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.143 Safari/537.36"
5th rowMozilla/5.0 (Windows NT 6.1; WOW64; rv:31.0) Gecko/20100101 Firefox/31.0
ValueCountFrequency (%)
mozilla/5.0528005
 
8.9%
like423151
 
7.1%
gecko392666
 
6.6%
khtml376525
 
6.3%
applewebkit/537.36275306
 
4.6%
safari/537.36275306
 
4.6%
windows270433
 
4.6%
nt270433
 
4.6%
os263029
 
4.4%
wow64244545
 
4.1%
Other values (87)2611635
44.0%
2025-10-06T14:02:04.581160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5403029
 
9.8%
.3054166
 
5.5%
i2558574
 
4.6%
e2493717
 
4.5%
l2110007
 
3.8%
o2083060
 
3.8%
02000294
 
3.6%
31996021
 
3.6%
/1958131
 
3.6%
51776953
 
3.2%
Other values (52)29709045
53.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)55142997
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5403029
 
9.8%
.3054166
 
5.5%
i2558574
 
4.6%
e2493717
 
4.5%
l2110007
 
3.8%
o2083060
 
3.8%
02000294
 
3.6%
31996021
 
3.6%
/1958131
 
3.6%
51776953
 
3.2%
Other values (52)29709045
53.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)55142997
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5403029
 
9.8%
.3054166
 
5.5%
i2558574
 
4.6%
e2493717
 
4.5%
l2110007
 
3.8%
o2083060
 
3.8%
02000294
 
3.6%
31996021
 
3.6%
/1958131
 
3.6%
51776953
 
3.2%
Other values (52)29709045
53.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)55142997
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5403029
 
9.8%
.3054166
 
5.5%
i2558574
 
4.6%
e2493717
 
4.5%
l2110007
 
3.8%
o2083060
 
3.8%
02000294
 
3.6%
31996021
 
3.6%
/1958131
 
3.6%
51776953
 
3.2%
Other values (52)29709045
53.9%
Distinct275
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
2025-10-06T14:02:04.675856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length8
Mean length6.1544569
Min length2

Characters and Unicode

Total characters3249584
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMorales
2nd rowLarson
3rd rowSantiago
4th rowSantiago
5th rowCampos
ValueCountFrequency (%)
reed12767
 
2.4%
taylor12666
 
2.4%
stewart11720
 
2.2%
robinson11291
 
2.1%
johnson11170
 
2.1%
williams10291
 
1.9%
jones10075
 
1.9%
white9287
 
1.8%
cook8665
 
1.6%
larson7205
 
1.4%
Other values (265)422868
80.1%
2025-10-06T14:02:04.799524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e323463
 
10.0%
o308681
 
9.5%
n303111
 
9.3%
a269898
 
8.3%
r267747
 
8.2%
s196827
 
6.1%
l192133
 
5.9%
i158369
 
4.9%
t109353
 
3.4%
h93232
 
2.9%
Other values (36)1026770
31.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)3249584
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e323463
 
10.0%
o308681
 
9.5%
n303111
 
9.3%
a269898
 
8.3%
r267747
 
8.2%
s196827
 
6.1%
l192133
 
5.9%
i158369
 
4.9%
t109353
 
3.4%
h93232
 
2.9%
Other values (36)1026770
31.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3249584
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e323463
 
10.0%
o308681
 
9.5%
n303111
 
9.3%
a269898
 
8.3%
r267747
 
8.2%
s196827
 
6.1%
l192133
 
5.9%
i158369
 
4.9%
t109353
 
3.4%
h93232
 
2.9%
Other values (36)1026770
31.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3249584
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e323463
 
10.0%
o308681
 
9.5%
n303111
 
9.3%
a269898
 
8.3%
r267747
 
8.2%
s196827
 
6.1%
l192133
 
5.9%
i158369
 
4.9%
t109353
 
3.4%
h93232
 
2.9%
Other values (36)1026770
31.6%
Distinct345
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
2025-10-06T14:02:04.918388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length10
Mean length5.9241522
Min length3

Characters and Unicode

Total characters3127982
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJoseph
2nd rowSawyer
3rd rowMaverick
4th rowMaverick
5th rowGianna
ValueCountFrequency (%)
joseph13108
 
2.5%
riley10426
 
2.0%
sophia9245
 
1.8%
colin8736
 
1.7%
daniel7476
 
1.4%
lucero7448
 
1.4%
alex7400
 
1.4%
janiyah6810
 
1.3%
sofia6629
 
1.3%
everett6523
 
1.2%
Other values (335)444204
84.1%
2025-10-06T14:02:05.066860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a398961
12.8%
e305198
 
9.8%
n283718
 
9.1%
i252052
 
8.1%
l229182
 
7.3%
o162158
 
5.2%
r150706
 
4.8%
y143410
 
4.6%
s142229
 
4.5%
h93949
 
3.0%
Other values (40)966419
30.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)3127982
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a398961
12.8%
e305198
 
9.8%
n283718
 
9.1%
i252052
 
8.1%
l229182
 
7.3%
o162158
 
5.2%
r150706
 
4.8%
y143410
 
4.6%
s142229
 
4.5%
h93949
 
3.0%
Other values (40)966419
30.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3127982
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a398961
12.8%
e305198
 
9.8%
n283718
 
9.1%
i252052
 
8.1%
l229182
 
7.3%
o162158
 
5.2%
r150706
 
4.8%
y143410
 
4.6%
s142229
 
4.5%
h93949
 
3.0%
Other values (40)966419
30.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3127982
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a398961
12.8%
e305198
 
9.8%
n283718
 
9.1%
i252052
 
8.1%
l229182
 
7.3%
o162158
 
5.2%
r150706
 
4.8%
y143410
 
4.6%
s142229
 
4.5%
h93949
 
3.0%
Other values (40)966419
30.9%

registration
Real number (ℝ)

Distinct448
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5355234 × 1012
Minimum1.5098542 × 1012
Maximum1.5430739 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2025-10-06T14:02:05.099858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.5098542 × 1012
5-th percentile1.5290262 × 1012
Q11.5343678 × 1012
median1.5365559 × 1012
Q31.5376119 × 1012
95-th percentile1.5382898 × 1012
Maximum1.5430739 × 1012
Range3.3219681 × 1010
Interquartile range (IQR)3.244138 × 109

Descriptive statistics

Standard deviation3.0787255 × 109
Coefficient of variation (CV)0.0020050007
Kurtosis8.0389685
Mean1.5355234 × 1012
Median Absolute Deviation (MAD)1.341543 × 109
Skewness-2.2673353
Sum8.1076404 × 1017
Variance9.4785507 × 1018
MonotonicityNot monotonic
2025-10-06T14:02:05.131579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15364039720009767
 
1.8%
15366421090007448
 
1.4%
15375230640007309
 
1.4%
15350663800006842
 
1.3%
15381784480006810
 
1.3%
15360822610006184
 
1.2%
15273411640006019
 
1.1%
15331757100005965
 
1.1%
15368594130005678
 
1.1%
15356234660005511
 
1.0%
Other values (438)460472
87.2%
ValueCountFrequency (%)
1509854193000418
 
0.1%
15193977130001123
0.2%
15213806750001408
0.3%
1522076012000523
 
0.1%
15227933340001616
0.3%
1523464964000620
 
0.1%
1523721066000797
0.2%
15237775210001447
0.3%
15267392060001982
0.4%
1526838391000366
 
0.1%
ValueCountFrequency (%)
1543073874000128
 
< 0.1%
1542205030000100
 
< 0.1%
15410418650001108
 
0.2%
1539437340000228
 
< 0.1%
15383473060001974
0.4%
15383367710004816
0.9%
15383347920001389
 
0.3%
15383338290002072
0.4%
15383332220001772
 
0.3%
153833231000053
 
< 0.1%

gender
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size515.9 KiB
M
302612 
F
225393 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters528005
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowF

Common Values

ValueCountFrequency (%)
M302612
57.3%
F225393
42.7%

Length

2025-10-06T14:02:05.158824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T14:02:05.173132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
m302612
57.3%
f225393
42.7%

Most occurring characters

ValueCountFrequency (%)
M302612
57.3%
F225393
42.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)528005
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M302612
57.3%
F225393
42.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)528005
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M302612
57.3%
F225393
42.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)528005
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M302612
57.3%
F225393
42.7%

artist
Text

Missing 

Distinct21247
Distinct (%)4.9%
Missing95128
Missing (%)18.0%
Memory size4.0 MiB
2025-10-06T14:02:05.237941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length318
Median length156
Mean length13.210217
Min length1

Characters and Unicode

Total characters5718399
Distinct characters132
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5729 ?
Unique (%)1.3%

Sample

1st rowMartin Orford
2nd rowJohn Brown's Body
3rd rowAfroman
4th rowLily Allen
5th rowCarter USM
ValueCountFrequency (%)
the50522
 
5.3%
28592
 
3.0%
of11040
 
1.2%
and6533
 
0.7%
featuring5193
 
0.5%
black4569
 
0.5%
kings4031
 
0.4%
leon3551
 
0.4%
coldplay3445
 
0.4%
john3407
 
0.4%
Other values (20001)837917
87.4%
2025-10-06T14:02:05.346168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e536697
 
9.4%
526051
 
9.2%
a424852
 
7.4%
i341369
 
6.0%
n330068
 
5.8%
o304441
 
5.3%
r302742
 
5.3%
l245870
 
4.3%
s236683
 
4.1%
t218714
 
3.8%
Other values (122)2250912
39.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)5718399
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e536697
 
9.4%
526051
 
9.2%
a424852
 
7.4%
i341369
 
6.0%
n330068
 
5.8%
o304441
 
5.3%
r302742
 
5.3%
l245870
 
4.3%
s236683
 
4.1%
t218714
 
3.8%
Other values (122)2250912
39.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5718399
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e536697
 
9.4%
526051
 
9.2%
a424852
 
7.4%
i341369
 
6.0%
n330068
 
5.8%
o304441
 
5.3%
r302742
 
5.3%
l245870
 
4.3%
s236683
 
4.1%
t218714
 
3.8%
Other values (122)2250912
39.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5718399
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e536697
 
9.4%
526051
 
9.2%
a424852
 
7.4%
i341369
 
6.0%
n330068
 
5.8%
o304441
 
5.3%
r302742
 
5.3%
l245870
 
4.3%
s236683
 
4.1%
t218714
 
3.8%
Other values (122)2250912
39.4%

song
Text

Missing 

Distinct80279
Distinct (%)18.5%
Missing95128
Missing (%)18.0%
Memory size4.0 MiB
2025-10-06T14:02:05.459319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length214
Median length140
Mean length17.901175
Min length1

Characters and Unicode

Total characters7749007
Distinct characters157
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37236 ?
Unique (%)8.6%

Sample

1st rowGrand Designs
2nd rowBulls
3rd rowBecause I Got High
4th rowSmile (Radio Edit)
5th rowAirplane Food
ValueCountFrequency (%)
the54800
 
3.9%
version43554
 
3.1%
album33820
 
2.4%
you24547
 
1.8%
a18325
 
1.3%
of17999
 
1.3%
me17359
 
1.2%
i17101
 
1.2%
in15648
 
1.1%
to14754
 
1.1%
Other values (38097)1144011
81.6%
2025-10-06T14:02:05.605738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
971832
 
12.5%
e717767
 
9.3%
o469008
 
6.1%
n402660
 
5.2%
i402593
 
5.2%
a400952
 
5.2%
r361702
 
4.7%
t313249
 
4.0%
s274727
 
3.5%
l273662
 
3.5%
Other values (147)3160855
40.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)7749007
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
971832
 
12.5%
e717767
 
9.3%
o469008
 
6.1%
n402660
 
5.2%
i402593
 
5.2%
a400952
 
5.2%
r361702
 
4.7%
t313249
 
4.0%
s274727
 
3.5%
l273662
 
3.5%
Other values (147)3160855
40.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)7749007
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
971832
 
12.5%
e717767
 
9.3%
o469008
 
6.1%
n402660
 
5.2%
i402593
 
5.2%
a400952
 
5.2%
r361702
 
4.7%
t313249
 
4.0%
s274727
 
3.5%
l273662
 
3.5%
Other values (147)3160855
40.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)7749007
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
971832
 
12.5%
e717767
 
9.3%
o469008
 
6.1%
n402660
 
5.2%
i402593
 
5.2%
a400952
 
5.2%
r361702
 
4.7%
t313249
 
4.0%
s274727
 
3.5%
l273662
 
3.5%
Other values (147)3160855
40.8%

length
Real number (ℝ)

High correlation  Missing 

Distinct16679
Distinct (%)3.9%
Missing95128
Missing (%)18.0%
Infinite0
Infinite (%)0.0%
Mean248.66459
Minimum0.78322
Maximum3024.6657
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2025-10-06T14:02:05.638032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.78322
5-th percentile138.16118
Q1199.3922
median234.00444
Q3276.79302
95-th percentile402.76591
Maximum3024.6657
Range3023.8825
Interquartile range (IQR)77.40082

Descriptive statistics

Standard deviation98.41267
Coefficient of variation (CV)0.39576471
Kurtosis76.191342
Mean248.66459
Median Absolute Deviation (MAD)38.03429
Skewness5.0288732
Sum1.0764118 × 108
Variance9685.0535
MonotonicityNot monotonic
2025-10-06T14:02:05.715797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
239.30732320
 
0.4%
348.577511962
 
0.4%
201.795461735
 
0.3%
655.777511343
 
0.3%
277.158731239
 
0.2%
219.663221069
 
0.2%
224.678731049
 
0.2%
220.890981023
 
0.2%
252.21179896
 
0.2%
236.09424870
 
0.2%
Other values (16669)419371
79.4%
(Missing)95128
 
18.0%
ValueCountFrequency (%)
0.783221
 
< 0.1%
1.122813
< 0.1%
2.167711
 
< 0.1%
3.944041
 
< 0.1%
4.048535
< 0.1%
4.179141
 
< 0.1%
4.83222
 
< 0.1%
5.537511
 
< 0.1%
6.34732
 
< 0.1%
6.660773
< 0.1%
ValueCountFrequency (%)
3024.6656710
< 0.1%
2960.58731
 
< 0.1%
2743.091791
 
< 0.1%
2732.27711
 
< 0.1%
2709.23711
 
< 0.1%
2626.455061
 
< 0.1%
2594.8730214
< 0.1%
2520.9987321
< 0.1%
2441.194651
 
< 0.1%
2390.203631
 
< 0.1%

ts_dt
Date

Distinct499351
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
Minimum2018-10-01 00:00:11+00:00
Maximum2018-12-01 00:01:06+00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-06T14:02:05.748507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T14:02:05.781863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct448
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
Minimum2017-11-05 03:56:33+00:00
Maximum2018-11-24 15:37:54+00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-06T14:02:05.812238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T14:02:05.844297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

os
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
Windows
270433 
macOS
232544 
Linux
 
25028

Length

Max length7
Median length7
Mean length6.0243577
Min length5

Characters and Unicode

Total characters3180891
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmacOS
2nd rowmacOS
3rd rowmacOS
4th rowmacOS
5th rowWindows

Common Values

ValueCountFrequency (%)
Windows270433
51.2%
macOS232544
44.0%
Linux25028
 
4.7%

Length

2025-10-06T14:02:05.878216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T14:02:05.897751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
windows270433
51.2%
macos232544
44.0%
linux25028
 
4.7%

Most occurring characters

ValueCountFrequency (%)
i295461
9.3%
n295461
9.3%
W270433
8.5%
d270433
8.5%
o270433
8.5%
w270433
8.5%
s270433
8.5%
m232544
7.3%
a232544
7.3%
c232544
7.3%
Other values (5)540172
17.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)3180891
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i295461
9.3%
n295461
9.3%
W270433
8.5%
d270433
8.5%
o270433
8.5%
w270433
8.5%
s270433
8.5%
m232544
7.3%
a232544
7.3%
c232544
7.3%
Other values (5)540172
17.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3180891
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i295461
9.3%
n295461
9.3%
W270433
8.5%
d270433
8.5%
o270433
8.5%
w270433
8.5%
s270433
8.5%
m232544
7.3%
a232544
7.3%
c232544
7.3%
Other values (5)540172
17.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3180891
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i295461
9.3%
n295461
9.3%
W270433
8.5%
d270433
8.5%
o270433
8.5%
w270433
8.5%
s270433
8.5%
m232544
7.3%
a232544
7.3%
c232544
7.3%
Other values (5)540172
17.0%

browser
Categorical

High correlation  Missing 

Distinct3
Distinct (%)< 0.1%
Missing37918
Missing (%)7.2%
Memory size4.0 MiB
Chrome
275306 
Firefox
113562 
Safari
101219 

Length

Max length7
Median length6
Mean length6.231718
Min length6

Characters and Unicode

Total characters3054084
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowChrome
2nd rowChrome
3rd rowChrome
4th rowChrome
5th rowFirefox

Common Values

ValueCountFrequency (%)
Chrome275306
52.1%
Firefox113562
21.5%
Safari101219
 
19.2%
(Missing)37918
 
7.2%

Length

2025-10-06T14:02:05.919502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T14:02:05.936482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
chrome275306
56.2%
firefox113562
23.2%
safari101219
 
20.7%

Most occurring characters

ValueCountFrequency (%)
r490087
16.0%
o388868
12.7%
e388868
12.7%
C275306
9.0%
h275306
9.0%
m275306
9.0%
i214781
7.0%
f214781
7.0%
a202438
6.6%
F113562
 
3.7%
Other values (2)214781
7.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)3054084
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r490087
16.0%
o388868
12.7%
e388868
12.7%
C275306
9.0%
h275306
9.0%
m275306
9.0%
i214781
7.0%
f214781
7.0%
a202438
6.6%
F113562
 
3.7%
Other values (2)214781
7.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3054084
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r490087
16.0%
o388868
12.7%
e388868
12.7%
C275306
9.0%
h275306
9.0%
m275306
9.0%
i214781
7.0%
f214781
7.0%
a202438
6.6%
F113562
 
3.7%
Other values (2)214781
7.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3054084
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r490087
16.0%
o388868
12.7%
e388868
12.7%
C275306
9.0%
h275306
9.0%
m275306
9.0%
i214781
7.0%
f214781
7.0%
a202438
6.6%
F113562
 
3.7%
Other values (2)214781
7.0%

device
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
Desktop
497520 
Mobile
 
30485

Length

Max length7
Median length7
Mean length6.9422638
Min length6

Characters and Unicode

Total characters3665550
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDesktop
2nd rowDesktop
3rd rowDesktop
4th rowDesktop
5th rowDesktop

Common Values

ValueCountFrequency (%)
Desktop497520
94.2%
Mobile30485
 
5.8%

Length

2025-10-06T14:02:05.958389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T14:02:05.972957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
desktop497520
94.2%
mobile30485
 
5.8%

Most occurring characters

ValueCountFrequency (%)
e528005
14.4%
o528005
14.4%
D497520
13.6%
s497520
13.6%
k497520
13.6%
t497520
13.6%
p497520
13.6%
M30485
 
0.8%
b30485
 
0.8%
i30485
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)3665550
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e528005
14.4%
o528005
14.4%
D497520
13.6%
s497520
13.6%
k497520
13.6%
t497520
13.6%
p497520
13.6%
M30485
 
0.8%
b30485
 
0.8%
i30485
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3665550
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e528005
14.4%
o528005
14.4%
D497520
13.6%
s497520
13.6%
k497520
13.6%
t497520
13.6%
p497520
13.6%
M30485
 
0.8%
b30485
 
0.8%
i30485
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3665550
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e528005
14.4%
o528005
14.4%
D497520
13.6%
s497520
13.6%
k497520
13.6%
t497520
13.6%
p497520
13.6%
M30485
 
0.8%
b30485
 
0.8%
i30485
 
0.8%

city
Text

Distinct187
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
2025-10-06T14:02:06.033651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length42
Median length32
Mean length20.136891
Min length4

Characters and Unicode

Total characters10632379
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCorpus Christi
2nd rowHouston-The Woodlands-Sugar Land
3rd rowOrlando-Kissimmee-Sanford
4th rowOrlando-Kissimmee-Sanford
5th rowMobile
ValueCountFrequency (%)
city51680
 
5.8%
new45353
 
5.1%
york-newark-jersey40156
 
4.5%
los34278
 
3.9%
angeles-long34278
 
3.9%
beach-anaheim34278
 
3.9%
san18557
 
2.1%
boston-cambridge-newton17574
 
2.0%
chicago-naperville-elgin15194
 
1.7%
francisco-oakland-hayward11428
 
1.3%
Other values (231)587234
66.0%
2025-10-06T14:02:06.136308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e969488
 
9.1%
a877940
 
8.3%
o742296
 
7.0%
n722917
 
6.8%
-629150
 
5.9%
r620281
 
5.8%
i553202
 
5.2%
l546456
 
5.1%
s490164
 
4.6%
t464881
 
4.4%
Other values (44)4015604
37.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)10632379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e969488
 
9.1%
a877940
 
8.3%
o742296
 
7.0%
n722917
 
6.8%
-629150
 
5.9%
r620281
 
5.8%
i553202
 
5.2%
l546456
 
5.1%
s490164
 
4.6%
t464881
 
4.4%
Other values (44)4015604
37.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10632379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e969488
 
9.1%
a877940
 
8.3%
o742296
 
7.0%
n722917
 
6.8%
-629150
 
5.9%
r620281
 
5.8%
i553202
 
5.2%
l546456
 
5.1%
s490164
 
4.6%
t464881
 
4.4%
Other values (44)4015604
37.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10632379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e969488
 
9.1%
a877940
 
8.3%
o742296
 
7.0%
n722917
 
6.8%
-629150
 
5.9%
r620281
 
5.8%
i553202
 
5.2%
l546456
 
5.1%
s490164
 
4.6%
t464881
 
4.4%
Other values (44)4015604
37.8%

region
Categorical

High correlation  Missing 

Distinct43
Distinct (%)< 0.1%
Missing152015
Missing (%)28.8%
Memory size4.0 MiB
CA
78171 
TX
42442 
FL
32129 
NC
 
18024
GA
 
16041
Other values (38)
189183 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters751980
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTX
2nd rowTX
3rd rowFL
4th rowFL
5th rowAL

Common Values

ValueCountFrequency (%)
CA78171
14.8%
TX42442
 
8.0%
FL32129
 
6.1%
NC18024
 
3.4%
GA16041
 
3.0%
AZ13997
 
2.7%
MI13724
 
2.6%
CO12485
 
2.4%
WI12273
 
2.3%
TN11090
 
2.1%
Other values (33)125614
23.8%
(Missing)152015
28.8%

Length

2025-10-06T14:02:06.165250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca78171
20.8%
tx42442
 
11.3%
fl32129
 
8.5%
nc18024
 
4.8%
ga16041
 
4.3%
az13997
 
3.7%
mi13724
 
3.7%
co12485
 
3.3%
wi12273
 
3.3%
tn11090
 
2.9%
Other values (33)125614
33.4%

Most occurring characters

ValueCountFrequency (%)
A151684
20.2%
C116170
15.4%
T66259
8.8%
N63275
8.4%
L52796
 
7.0%
I43554
 
5.8%
X42442
 
5.6%
M34763
 
4.6%
F32129
 
4.3%
W22603
 
3.0%
Other values (14)126305
16.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)751980
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A151684
20.2%
C116170
15.4%
T66259
8.8%
N63275
8.4%
L52796
 
7.0%
I43554
 
5.8%
X42442
 
5.6%
M34763
 
4.6%
F32129
 
4.3%
W22603
 
3.0%
Other values (14)126305
16.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)751980
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A151684
20.2%
C116170
15.4%
T66259
8.8%
N63275
8.4%
L52796
 
7.0%
I43554
 
5.8%
X42442
 
5.6%
M34763
 
4.6%
F32129
 
4.3%
W22603
 
3.0%
Other values (14)126305
16.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)751980
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A151684
20.2%
C116170
15.4%
T66259
8.8%
N63275
8.4%
L52796
 
7.0%
I43554
 
5.8%
X42442
 
5.6%
M34763
 
4.6%
F32129
 
4.3%
W22603
 
3.0%
Other values (14)126305
16.8%

country
Categorical

High correlation  Imbalance 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
USA
375990 
NY-NJ-PA
40156 
MA-NH
 
17574
IL-IN-WI
 
15194
MN-WI
 
7479
Other values (23)
71612 

Length

Max length11
Median length3
Mean length4.0611453
Min length3

Characters and Unicode

Total characters2144305
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSA
2nd rowUSA
3rd rowUSA
4th rowUSA
5th rowUSA

Common Values

ValueCountFrequency (%)
USA375990
71.2%
NY-NJ-PA40156
 
7.6%
MA-NH17574
 
3.3%
IL-IN-WI15194
 
2.9%
MN-WI7479
 
1.4%
KY-IN7457
 
1.4%
PA-NJ-DE-MD6962
 
1.3%
MO-KS6956
 
1.3%
VA-NC6624
 
1.3%
DC-VA-MD-WV6181
 
1.2%
Other values (18)37432
 
7.1%

Length

2025-10-06T14:02:06.190889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
usa375990
71.2%
ny-nj-pa40156
 
7.6%
ma-nh17574
 
3.3%
il-in-wi15194
 
2.9%
mn-wi7479
 
1.4%
ky-in7457
 
1.4%
pa-nj-de-md6962
 
1.3%
mo-ks6956
 
1.3%
va-nc6624
 
1.3%
dc-va-md-wv6181
 
1.2%
Other values (18)37432
 
7.1%

Most occurring characters

ValueCountFrequency (%)
A474753
22.1%
S390022
18.2%
U376184
17.5%
-237435
11.1%
N158604
 
7.4%
I77055
 
3.6%
M64932
 
3.0%
J49735
 
2.3%
P49735
 
2.3%
Y48927
 
2.3%
Other values (12)216923
10.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)2144305
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A474753
22.1%
S390022
18.2%
U376184
17.5%
-237435
11.1%
N158604
 
7.4%
I77055
 
3.6%
M64932
 
3.0%
J49735
 
2.3%
P49735
 
2.3%
Y48927
 
2.3%
Other values (12)216923
10.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2144305
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A474753
22.1%
S390022
18.2%
U376184
17.5%
-237435
11.1%
N158604
 
7.4%
I77055
 
3.6%
M64932
 
3.0%
J49735
 
2.3%
P49735
 
2.3%
Y48927
 
2.3%
Other values (12)216923
10.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2144305
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A474753
22.1%
S390022
18.2%
U376184
17.5%
-237435
11.1%
N158604
 
7.4%
I77055
 
3.6%
M64932
 
3.0%
J49735
 
2.3%
P49735
 
2.3%
Y48927
 
2.3%
Other values (12)216923
10.1%

Interactions

2025-10-06T14:02:01.155471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T14:01:56.044423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T14:01:56.694410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T14:01:59.284606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T14:01:59.901590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T14:02:00.524673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T14:02:01.234326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T14:01:56.135371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T14:01:57.090243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T14:01:59.370022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T14:01:59.986936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T14:02:00.613317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T14:02:01.610095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T14:01:56.488500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T14:01:57.798075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T14:01:59.736166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T14:02:00.359566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T14:02:00.987174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T14:02:01.650590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T14:01:56.525772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T14:01:58.158332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T14:01:59.777556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T14:02:00.398908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T14:02:01.030715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T14:02:01.689954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T14:01:56.567502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T14:01:58.576959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T14:01:59.818531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T14:02:00.441225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T14:02:01.073306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T14:02:01.728532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T14:01:56.606983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T14:01:58.971613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T14:01:59.858708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T14:02:00.482781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T14:02:01.114953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-06T14:02:06.216109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
authbrowsercountrydevicegenderitemInSessionlengthlevelmethodospageregionregistrationsessionIdstatustsuserId
auth1.0000.0010.0040.0020.0000.0011.0000.0030.0480.0001.0000.0110.0030.0080.0040.0010.058
browser0.0011.0000.3340.5050.0050.0510.0000.0190.0000.4000.0060.4410.1350.0650.0010.0481.000
country0.0040.3341.0000.4440.3050.0540.0000.1710.0240.3300.0101.0000.2660.1240.0040.0921.000
device0.0020.5050.4441.0000.0250.0210.0000.0160.0010.2790.0070.1920.2120.0360.0030.0531.000
gender0.0000.0050.3050.0251.0000.0320.0000.0020.0000.1050.0030.4320.1260.0800.0000.0371.000
itemInSession0.0010.0510.0540.0210.0321.0000.0010.2810.0440.0380.0240.079-0.0050.1420.0040.076-0.089
length1.0000.0000.0000.0000.0000.0011.0000.0021.0000.0001.0000.001-0.000-0.0031.000-0.0030.000
level0.0030.0190.1710.0160.0020.2810.0021.0000.1240.0010.2500.2110.0800.2520.0050.2650.688
method0.0480.0000.0240.0010.0000.0441.0000.1241.0000.0001.0000.0310.0120.0370.1380.0320.094
os0.0000.4000.3300.2790.1050.0380.0000.0010.0001.0000.0010.5580.1760.0640.0000.0551.000
page1.0000.0060.0100.0070.0030.0241.0000.2501.0000.0011.0000.0150.0080.0271.0000.0220.051
region0.0110.4411.0000.1920.4320.0790.0010.2110.0310.5580.0151.0000.4610.1920.0130.1471.000
registration0.0030.1350.2660.2120.126-0.005-0.0000.0800.0120.1760.0080.4611.000-0.0540.0040.0190.028
sessionId0.0080.0650.1240.0360.0800.142-0.0030.2520.0370.0640.0270.192-0.0541.0000.0190.693-0.545
status0.0040.0010.0040.0030.0000.0041.0000.0050.1380.0001.0000.0130.0040.0191.0000.0020.028
ts0.0010.0480.0920.0530.0370.076-0.0030.2650.0320.0550.0220.1470.0190.6930.0021.000-0.063
userId0.0581.0001.0001.0001.000-0.0890.0000.6880.0941.0000.0511.0000.028-0.5450.028-0.0631.000

Missing values

2025-10-06T14:02:01.896428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-06T14:02:02.412275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-10-06T14:02:03.340045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

tsuserIdsessionIdpageauthmethodstatuslevelitemInSessionlocationuserAgentlastNamefirstNameregistrationgenderartistsonglengthts_dtregistration_dtosbrowserdevicecityregioncountry
01538352011000293292NextSongLogged InPUT200free20Corpus Christi, TX"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.125 Safari/537.36"MoralesJoseph1532063507000MMartin OrfordGrand Designs597.550572018-10-01 00:00:11+00:002018-07-20 05:11:47+00:00macOSChromeDesktopCorpus ChristiTXUSA
115383520250009897NextSongLogged InPUT200free74Houston-The Woodlands-Sugar Land, TX"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.143 Safari/537.36"LarsonSawyer1538069638000MJohn Brown's BodyBulls380.211792018-10-01 00:00:25+00:002018-09-27 17:33:58+00:00macOSChromeDesktopHouston-The Woodlands-Sugar LandTXUSA
21538352118000179178NextSongLogged InPUT200paid184Orlando-Kissimmee-Sanford, FL"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.143 Safari/537.36"SantiagoMaverick1535953455000MAfromanBecause I Got High202.370162018-10-01 00:01:58+00:002018-09-03 05:44:15+00:00macOSChromeDesktopOrlando-Kissimmee-SanfordFLUSA
31538352119000179178LogoutLogged InPUT307paid185Orlando-Kissimmee-Sanford, FL"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.143 Safari/537.36"SantiagoMaverick1535953455000M<NA><NA>NaN2018-10-01 00:01:59+00:002018-09-03 05:44:15+00:00macOSChromeDesktopOrlando-Kissimmee-SanfordFLUSA
41538352124000246245NextSongLogged InPUT200paid22Mobile, ALMozilla/5.0 (Windows NT 6.1; WOW64; rv:31.0) Gecko/20100101 Firefox/31.0CamposGianna1535931018000FLily AllenSmile (Radio Edit)194.533422018-10-01 00:02:04+00:002018-09-02 23:30:18+00:00WindowsFirefoxDesktopMobileALUSA
51538352125000163162NextSongLogged InPUT200paid266Rochester, MN"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/37.0.2062.94 Safari/537.36"GordonSofia1533175710000FCarter USMAirplane Food138.291792018-10-01 00:02:05+00:002018-08-02 02:08:30+00:00macOSChromeDesktopRochesterMNUSA
61538352176000179178HomeLogged InGET200paid190Orlando-Kissimmee-Sanford, FL"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.143 Safari/537.36"SantiagoMaverick1535953455000M<NA><NA>NaN2018-10-01 00:02:56+00:002018-09-03 05:44:15+00:00macOSChromeDesktopOrlando-Kissimmee-SanfordFLUSA
71538352215000175442NextSongLogged InPUT200free107El Campo, TX"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/37.0.2062.94 Safari/537.36"CastanedaLacey1537536032000FAerosmithWalk This Way220.394652018-10-01 00:03:35+00:002018-09-21 13:20:32+00:00macOSChromeDesktopEl CampoTXUSA
81538352241000100497HomeLogged InGET200free0Dallas-Fort Worth-Arlington, TXMozilla/5.0 (Windows NT 6.1; WOW64; rv:30.0) Gecko/20100101 Firefox/30.0LarsonColin1537982255000M<NA><NA>NaN2018-10-01 00:04:01+00:002018-09-26 17:17:35+00:00WindowsFirefoxDesktopDallas-Fort Worth-ArlingtonTXUSA
91538352259000100497NextSongLogged InPUT200free1Dallas-Fort Worth-Arlington, TXMozilla/5.0 (Windows NT 6.1; WOW64; rv:30.0) Gecko/20100101 Firefox/30.0LarsonColin1537982255000MAmy WinehouseTeach Me Tonight201.508122018-10-01 00:04:19+00:002018-09-26 17:17:35+00:00WindowsFirefoxDesktopDallas-Fort Worth-ArlingtonTXUSA
tsuserIdsessionIdpageauthmethodstatuslevelitemInSessionlocationuserAgentlastNamefirstNameregistrationgenderartistsonglengthts_dtregistration_dtosbrowserdevicecityregioncountry
5279951543622376000274518NextSongLogged InPUT200paid177Hartford-West Hartford-East Hartford, CT"Mozilla/5.0 (Windows NT 6.3; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.143 Safari/537.36"RaymondEthan1534245996000MMarilyn MansonThe Beautiful People218.827302018-11-30 23:59:36+00:002018-08-14 11:26:36+00:00WindowsChromeDesktopHartford-West Hartford-East HartfordCTUSA
5279961543622378000864704NextSongLogged InPUT200paid226Jacksonville, FLMozilla/5.0 (Windows NT 6.1; WOW64; rv:31.0) Gecko/20100101 Firefox/31.0ReillyJaxon1531760527000MThe Derek Trucks BandI'd Rather Be Blind_ Crippled And Crazy276.531792018-11-30 23:59:38+00:002018-07-16 17:02:07+00:00WindowsFirefoxDesktopJacksonvilleFLUSA
52799715436223790001534683NextSongLogged InPUT200paid197Greensboro-High Point, NC"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.125 Safari/537.36"PooleAndrew1537085185000MBenny NeymanWaarom Fluister Ik Je Naam Nog224.757102018-11-30 23:59:39+00:002018-09-16 08:06:25+00:00WindowsChromeDesktopGreensboro-High PointNCUSA
5279981543622379000464429NextSongLogged InPUT200free20Los Angeles-Long Beach-Anaheim, CA"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.143 Safari/537.36"RodriguezAdelaida1537767796000FDwight YoakamYou're The One239.307302018-11-30 23:59:39+00:002018-09-24 05:43:16+00:00macOSChromeDesktopLos Angeles-Long Beach-AnaheimCAUSA
5279991543622392000300038966NextSongLogged InPUT200paid36Lakeland-Winter Haven, FL"Mozilla/5.0 (iPhone; CPU iPhone OS 7_1_2 like Mac OS X) AppleWebKit/537.51.2 (KHTML, like Gecko) Version/7.0 Mobile/11D257 Safari/9537.53"ReedAiden1535616235000MHinderLips Of An Angel262.059952018-11-30 23:59:52+00:002018-08-30 08:03:55+00:00macOSSafariMobileLakeland-Winter HavenFLUSA
52800015436223930001634669HomeLogged InGET200paid21Rochester, MN"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/37.0.2062.94 Safari/537.36"GordonSofia1533175710000F<NA><NA>NaN2018-11-30 23:59:53+00:002018-08-02 02:08:30+00:00macOSChromeDesktopRochesterMNUSA
5280011543622401000300015981NextSongLogged InPUT200paid109San Antonio-New Braunfels, TX"Mozilla/5.0 (iPhone; CPU iPhone OS 7_1_2 like Mac OS X) AppleWebKit/537.51.2 (KHTML, like Gecko) Version/7.0 Mobile/11D257 Safari/9537.53"WhiteJoshua1528780738000MJack JohnsonDo You Remember144.064852018-12-01 00:00:01+00:002018-06-12 05:18:58+00:00macOSSafariMobileSan Antonio-New BraunfelsTXUSA
52800215436224040001634669NextSongLogged InPUT200paid22Rochester, MN"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/37.0.2062.94 Safari/537.36"GordonSofia1533175710000FDeep Blue SomethingBreakfast At Tiffany's257.227302018-12-01 00:00:04+00:002018-08-02 02:08:30+00:00macOSChromeDesktopRochesterMNUSA
5280031543622405000100008405NextSongLogged InPUT200paid78Los Angeles-Long Beach-Anaheim, CAMozilla/5.0 (Macintosh; Intel Mac OS X 10.7; rv:31.0) Gecko/20100101 Firefox/31.0StoneBryanna1537440271000FCreedence Clearwater RevivalLong As I Can See The Light213.106492018-12-01 00:00:05+00:002018-09-20 10:44:31+00:00macOSFirefoxDesktopLos Angeles-Long Beach-AnaheimCAUSA
5280041543622466000200030666NextSongLogged InPUT200paid95Chicago-Naperville-Elgin, IL-IN-WI"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_4) AppleWebKit/537.78.2 (KHTML, like Gecko) Version/7.0.6 Safari/537.78.2"SandovalAven1538334792000MLimp BizkitThe Truth325.511382018-12-01 00:01:06+00:002018-09-30 19:13:12+00:00macOSSafariDesktopChicago-Naperville-ElginNoneIL-IN-WI